To get something going with text (or any) classification algorithm is easy enough, all you need is an algorithm, such as Maximum Entropy or Naive Bayes, an implementation of each is available in many different flavors across various programming languages (I use NLTK on Python for text classification), and a bunch of already classified corpus data to train your algorithm on and that is it, you got yourself a basic classifier.

But the story rarely ends here, and to get any decent production-level performance or accuracy out of your classification algorithm, you’ll need to iteratively test your algorithm for optimum configuration, understand how different classes interact with each other, and diagnose any abnormality or irregularity you’re algorithm is experiencing.

In this post I hope to cover some basic mathematical tools for diagnosing and testing a classification algorithm, I will be taking a real life algorithm that I have worked as an example, and explore the various techniques we used to better understand how well it is performing, and when it is not performing, what is the underlying characteristic of this failure.

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NLTK (Natural Language Toolkit) is a Python library that allows developers and researchers to extract information and annotations from text, and run classification algorithms such as the Naive Bayes or Maximum Entropy, as well as many other interesting Natural Language tools and processing techniques.

The Maximum Entropy algorithm from NLTK comes in different flavours, this post will introduce the different Max Ent classification algorithm flavours supported by the NLTK library, as well as provide a compiled MEGAM binary on a Linux (Ubuntu) 64-bit machine, which is a requirement for running Max Ent NLTK classification on the megam algorithm.

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